Design of New Media Advertisement Visualization System under Recommendation Algorithm

The traditional advertising display method requires a lot of manpower for publicity, the input cost is high, the income is slow, and it is difficult to manage effectively. The purpose of this paper is to study the design of new media advertising visualization system based on recommendation algorithm. First of all, this paper summarizes the working methods of web crawler, elaborates the visualization tools in detail, and discusses the current situation and development of Weibo advertisement recommendation. On this basis, an improved algorithm is proposed. Design and develop a personalized advertising recommendation visualization system based on user interests. The main body of this system is the microblog platform. This paper conducts experiments on the advertisement recommendation algorithm by collecting microblog texts of different users. The results show that when the number of recommended advertisements is 20, the recommendation accuracy of the algorithm proposed in this paper is 93%, which is more accurate than the traditional recommendation algorithm. closer to user interests.